load(res_fname, verbose = T)
## Loading objects:
## res_alg
## res_arh
## res_bmi
## res_crd
## res_crd_wbmi
## res_dir
## res_fa
## res_mets_bd
## res_mets_bd_bmi
## res_mets_ics
## res_mets_ics_bmi
## res_mets_ocs
## res_mets_ocs_bmi
## res_normal
## res_red_mlk
## mets_info
## mets_pheno
## mets_pheno_asth
## mets_pheno_trim
## cer_list
## ceroth_list
## sm_list
## sph_list
## sphoth_list
## Model results to show
res_long <- rbind(res_crd[, model := "crude"],
res_bmi[, model := "BMI adjusted (n-pairs=293)"],
res_crd_wbmi[, model := "in subject w BMI (not BMI adjusted)"],
res_normal[, model := "exclude red/milky samples (n-pairs=545)"])
res_long[, dir_eff := case_when(or > 1 ~ "positive",
or <= 1 ~ "negative")]
res_long[, dir_eff := factor(dir_eff, levels = c("negative", "positive"))]
res_long[, model := factor(model, levels = c("crude", "BMI adjusted (n-pairs=293)", "in subject w BMI (not BMI adjusted)",
"exclude red/milky samples (n-pairs=545)"))]
res_long[, metabolite := factor(metabolite, levels = c(sphoth_list, sm_list, cer_list, ceroth_list))]
res_long[, rev_metabolite := factor(metabolite, levels = rev(levels(metabolite)))]
## Models results with minimal change from crude
res_unused <- rbind(res_crd[, model := "crude"],
res_arh[, model := "allergic rhinitis adjusted"],
res_fa[, model := "food allergy #Dx adjusted"],
res_alg[, model := "unspecified allergy #Dx adjusted"])
res_unused[, dir_eff := case_when(or > 1 ~ "positive",
or <= 1 ~ "negative")]
res_unused[, dir_eff := factor(dir_eff, levels = c("negative", "positive"))]
res_unused[, model := factor(model, levels = c("crude", "allergic rhinitis adjusted", "food allergy #Dx adjusted",
"unspecified allergy #Dx adjusted"))]
res_unused[, metabolite := factor(metabolite, levels = c(sphoth_list, sm_list, cer_list, ceroth_list))]
res_unused[, rev_metabolite := factor(metabolite, levels = rev(levels(metabolite)))]
res_crd[fdr_bh < alpha_thld, max(pval)]
## [1] 0.01291851
res_crd[fdr_bh >= alpha_thld, min(pval)]
## [1] 0.01539316
fdr_thld <- 0.014
bonf_thld <- 0.05/mets_info[, length(metabolite)]
ggplot(res_crd, aes(x = beta, y = -log10(pval))) +
geom_point(alpha = 0.4, size = 2.5) +
geom_hline(yintercept = -log10(alpha_thld), color = "black", linetype = 2) +
geom_hline(yintercept = -log10(fdr_thld), color = "#00798c", linetype = 2) +
geom_hline(yintercept = -log10(bonf_thld), color = "#edae49", linetype = 2) +
labs(title = "Crude conditional logistic model results",
x = expression('Estimated '*beta),
y = expression('-log'[10]*'('*italic(P)*'-value)')) +
annotate("text", x = 0, y = -log10(alpha_thld), label = "P_value == 0.05", parse = T,
vjust = 1.5, hjust = 0, color = "black", size = 4) +
annotate("text", x = 0, y = -log10(fdr_thld), label = "FDR_BH == 0.05", parse = T,
vjust = 1.5, hjust = 0, color = "#00798c", size = 4) +
annotate("text", x = 0, y = -log10(bonf_thld), label = "Bonferroni threshold", parse = F,
vjust = 1.5, hjust = 0, color = "#edae49", size = 4) +
geom_label_repel(aes(label = ifelse(pval < fdr_thld, metabolite, "")),
box.padding = 0.25,
point.padding = 0.5,
segment.color = 'grey50') +
theme_minimal() +
theme(title = element_text(size = 16),
axis.title = element_text(size = 12),
axis.text = element_text(size = 12))
ggsave(here(fig_dir, "volc_crd.jpg"), width = 12, height = 7)
ggplot(res_crd_wbmi, aes(x = beta, y = -log10(pval))) +
geom_point(alpha = 0.4, size = 2.5) +
geom_hline(yintercept = -log10(alpha_thld), color = "#00798c", linetype = 2) +
labs(title = "Crude conditional logistic model results in subjects with BMI (not BMI-adjusted)",
x = expression('Estimated '*beta),
y = expression('-log'[10]*'('*italic(P)*'-value)')) +
annotate("text", x = 0, y = -log10(alpha_thld), label = "P_value == 0.05", parse = T,
vjust = 1.5, hjust = 0, color = "#00798c", size = 4) +
geom_label_repel(aes(label = ifelse(pval < alpha_thld, metabolite, "")),
box.padding = 0.25,
point.padding = 0.5,
segment.color = 'grey50') +
theme_minimal() +
theme(title = element_text(size = 16),
axis.title = element_text(size = 12),
axis.text = element_text(size = 12))
ggsave(here(fig_dir, "volc_crd_subj_wbmi.jpg"), width = 12, height = 7)
res_normal[fdr_bh < alpha_thld, max(pval)]
## [1] 0.009423959
res_normal[fdr_bh >= alpha_thld, min(pval)]
## [1] 0.01132697
fdr_thld <- 0.01
ggplot(res_normal, aes(x = beta, y = -log10(pval))) +
geom_point(alpha = 0.4, size = 2.5) +
geom_hline(yintercept = -log10(alpha_thld), color = "black", linetype = 2) +
geom_hline(yintercept = -log10(fdr_thld), color = "#00798c", linetype = 2) +
geom_hline(yintercept = -log10(bonf_thld), color = "#edae49", linetype = 2) +
labs(title = "Crude conditional logistic model results - exclude red/milky samples",
x = expression('Estimated '*beta),
y = expression('-log'[10]*'('*italic(P)*'-value)')) +
annotate("text", x = 0, y = -log10(alpha_thld), label = "P_value == 0.05", parse = T,
vjust = 1.5, hjust = 0, color = "black", size = 4) +
annotate("text", x = 0, y = -log10(fdr_thld), label = "FDR_BH == 0.05", parse = T,
vjust = 1.5, hjust = 0, color = "#00798c", size = 4) +
annotate("text", x = 0, y = -log10(bonf_thld), label = "Bonferroni threshold", parse = F,
vjust = 1.5, hjust = 0, color = "#edae49", size = 4) +
geom_label_repel(aes(label = ifelse(pval < fdr_thld, metabolite, "")),
box.padding = 0.25,
point.padding = 0.5,
segment.color = 'grey50') +
theme_minimal() +
theme(title = element_text(size = 16),
axis.title = element_text(size = 12),
axis.text = element_text(size = 12))
ggsave(here(fig_dir, "volc_normal.jpg"), width = 12, height = 7)
ggplot(res_bmi, aes(x = beta, y = -log10(pval))) +
geom_point(alpha = 0.4, size = 2.5) +
geom_hline(yintercept = -log10(alpha_thld), color = "#00798c", linetype = 2) +
labs(title = "BMI-adjusted conditional logistic model results",
x = expression('Estimated '*beta),
y = expression('-log'[10]*'('*italic(P)*'-value)')) +
annotate("text", x = 0, y = -log10(alpha_thld), label = "P_value == 0.05", parse = T,
vjust = 1.5, hjust = -1.8, color = "#00798c", size = 4) +
geom_label_repel(aes(label = ifelse(pval < alpha_thld, metabolite, "")),
box.padding = 0.25,
point.padding = 0.5,
segment.color = 'grey50') +
theme_minimal() +
theme(title = element_text(size = 16),
axis.title = element_text(size = 12),
axis.text = element_text(size = 12))
ggsave(here(fig_dir, "volc_bmi.jpg"), width = 12, height = 7)
## ICS
ggplot(mets_pheno_trim[Group == "Asthmatic", ]) +
geom_histogram(aes(inhaled_steriod_prescriptions), bins = 30) +
labs(title = "Number of ICS prescriptions in asthmatics") +
theme_minimal() +
theme(title = element_text(size = 16),
axis.title = element_text(size = 12),
axis.text = element_text(size = 12))
ggsave(here(fig_dir, "ics_asthmatics.jpg"), width = 12, height = 7)
ggplot(res_mets_ics, aes(x = beta, y = -log10(pval))) +
geom_point(alpha = 0.4, size = 2.5) +
geom_hline(yintercept = -log10(alpha_thld), color = "#00798c", linetype = 2) +
labs(title = "Metabolites & number of ICS prescriptions - linear model results",
x = expression('Estimated '*beta),
y = expression('-log'[10]*'('*italic(P)*'-value)')) +
annotate("text", x = 0, y = -log10(alpha_thld), label = "P_value == 0.05", parse = T,
vjust = 1.5, hjust = -1.8, color = "#00798c", size = 4) +
geom_label_repel(aes(label = ifelse(pval < alpha_thld, metabolite, "")),
box.padding = 0.25,
point.padding = 0.5,
segment.color = 'grey50') +
theme_minimal() +
theme(title = element_text(size = 16),
axis.title = element_text(size = 12),
axis.text = element_text(size = 12))
ggsave(here(fig_dir, "volc_mets_ics.jpg"), width = 12, height = 7)
ggplot(res_mets_ics_bmi, aes(x = beta, y = -log10(pval))) +
geom_point(alpha = 0.4, size = 2.5) +
geom_hline(yintercept = -log10(alpha_thld), color = "#00798c", linetype = 2) +
labs(title = "Metabolites & number of ICS prescriptions - BMI-adjusted linear model results",
x = expression('Estimated '*beta),
y = expression('-log'[10]*'('*italic(P)*'-value)')) +
annotate("text", x = 0, y = -log10(alpha_thld), label = "P_value == 0.05", parse = T,
vjust = 1.5, hjust = -1.8, color = "#00798c", size = 4) +
geom_label_repel(aes(label = ifelse(pval < alpha_thld*2, metabolite, "")),
box.padding = 0.25,
point.padding = 0.5,
segment.color = 'grey50') +
theme_minimal() +
theme(title = element_text(size = 16),
axis.title = element_text(size = 12),
axis.text = element_text(size = 12))
ggsave(here(fig_dir, "volc_mets_ics_bmi.jpg"), width = 12, height = 7)
## OCS
ggplot(mets_pheno_trim[Group == "Asthmatic", ]) +
geom_histogram(aes(oral_steroid_prescriptions), bins = 30) +
labs(title = "Number of OCS prescriptions in asthmatics") +
theme_minimal() +
theme(title = element_text(size = 16),
axis.title = element_text(size = 12),
axis.text = element_text(size = 12))
ggsave(here(fig_dir, "ocs_asthmatics.jpg"), width = 12, height = 7)
ggplot(res_mets_ocs, aes(x = beta, y = -log10(pval))) +
geom_point(alpha = 0.4, size = 2.5) +
geom_hline(yintercept = -log10(alpha_thld), color = "#00798c", linetype = 2) +
labs(title = "Metabolites & number of OCS prescriptions - linear model results",
x = expression('Estimated '*beta),
y = expression('-log'[10]*'('*italic(P)*'-value)')) +
annotate("text", x = 0, y = -log10(alpha_thld), label = "P_value == 0.05", parse = T,
vjust = 1.5, hjust = -1.8, color = "#00798c", size = 4) +
geom_label_repel(aes(label = ifelse(pval < alpha_thld, metabolite, "")),
box.padding = 0.25,
point.padding = 0.5,
segment.color = 'grey50') +
theme_minimal() +
theme(title = element_text(size = 16),
axis.title = element_text(size = 12),
axis.text = element_text(size = 12))
ggsave(here(fig_dir, "volc_mets_ocs.jpg"), width = 12, height = 7)
ggplot(res_mets_ocs_bmi, aes(x = beta, y = -log10(pval))) +
geom_point(alpha = 0.4, size = 2.5) +
geom_hline(yintercept = -log10(alpha_thld), color = "#00798c", linetype = 2) +
labs(title = "Metabolites & number of OCS prescriptions - BMI-adjusted linear model results",
x = expression('Estimated '*beta),
y = expression('-log'[10]*'('*italic(P)*'-value)')) +
annotate("text", x = 0, y = -log10(alpha_thld), label = "P_value == 0.05", parse = T,
vjust = 1.5, hjust = -1.8, color = "#00798c", size = 4) +
geom_label_repel(aes(label = ifelse(pval < alpha_thld*2, metabolite, "")),
box.padding = 0.25,
point.padding = 0.5,
segment.color = 'grey50') +
theme_minimal() +
theme(title = element_text(size = 16),
axis.title = element_text(size = 12),
axis.text = element_text(size = 12))
ggsave(here(fig_dir, "volc_mets_ocs_bmi.jpg"), width = 12, height = 7)
## Bronchodilator
ggplot(mets_pheno_trim[Group == "Asthmatic", ]) +
geom_histogram(aes(bronchodialator_prescriptions), bins = 30) +
labs(title = "Number of bronchodilator prescriptions in asthmatics") +
theme_minimal() +
theme(title = element_text(size = 16),
axis.title = element_text(size = 12),
axis.text = element_text(size = 12))
ggsave(here(fig_dir, "bd_asthmatics.jpg"), width = 12, height = 7)
ggplot(res_mets_bd, aes(x = beta, y = -log10(pval))) +
geom_point(alpha = 0.4, size = 2.5) +
geom_hline(yintercept = -log10(alpha_thld), color = "#00798c", linetype = 2) +
labs(title = "Metabolites & number of bronchodilator prescriptions - linear model results",
x = expression('Estimated '*beta),
y = expression('-log'[10]*'('*italic(P)*'-value)')) +
annotate("text", x = 0, y = -log10(alpha_thld), label = "P_value == 0.05", parse = T,
vjust = 1.5, hjust = -1.8, color = "#00798c", size = 4) +
geom_label_repel(aes(label = ifelse(pval < alpha_thld, metabolite, "")),
box.padding = 0.25,
point.padding = 0.5,
segment.color = 'grey50') +
theme_minimal() +
theme(title = element_text(size = 16),
axis.title = element_text(size = 12),
axis.text = element_text(size = 12))
ggsave(here(fig_dir, "volc_mets_bd.jpg"), width = 12, height = 7)
ggplot(res_mets_bd_bmi, aes(x = beta, y = -log10(pval))) +
geom_point(alpha = 0.4, size = 2.5) +
geom_hline(yintercept = -log10(alpha_thld), color = "#00798c", linetype = 2) +
labs(title = "Metabolites & number of bronchodilator prescriptions - BMI-adjusted linear model results",
x = expression('Estimated '*beta),
y = expression('-log'[10]*'('*italic(P)*'-value)')) +
annotate("text", x = 0, y = -log10(alpha_thld), label = "P_value == 0.05", parse = T,
vjust = 1.5, hjust = -1.8, color = "#00798c", size = 4) +
geom_label_repel(aes(label = ifelse(pval < alpha_thld, metabolite, "")),
box.padding = 0.25,
point.padding = 0.5,
segment.color = 'grey50') +
theme_minimal() +
theme(title = element_text(size = 16),
axis.title = element_text(size = 12),
axis.text = element_text(size = 12))
ggsave(here(fig_dir, "volc_mets_bd_bmi.jpg"), width = 12, height = 7)
ggplot(res_long[metabolite %in% sphoth_list, ], aes(x = rev_metabolite, y = or)) +
facet_grid(. ~ model) +
geom_errorbar(aes(ymin = lower95, ymax = upper95, color = dir_eff),
width = 0.25) +
geom_pointrange(aes(ymin = lower95, ymax = upper95, color = dir_eff),
size = 0.5) +
scale_colour_hp_d(option = "Ravenclaw", name = "direction of effect") +
geom_hline(yintercept = 1, linetype = 2) +
labs(title = "Metabolites & asthma status",
x = "Metabolite", y = "Odds Ratio (95% Confidence Interval)") +
theme_minimal() +
theme(title = element_text(size = 16),
strip.text = element_text(size = 12),
axis.title = element_text(size = 12),
axis.text = element_text(size = 12),
legend.position = "bottom",
legend.title = element_text(size = 12),
legend.text = element_text(size = 12)) +
scale_y_continuous(trans = log_trans(),
breaks = axisTicks(log(range(res_long[metabolite %in% sphoth_list, or])),
log = TRUE)) +
coord_flip()
ggsave(here(fig_dir, "sphoth_or95ci.jpg"), width = 14, height = 7)
ggplot(res_long[metabolite %in% sm_list, ], aes(x = rev_metabolite, y = or)) +
facet_grid(. ~ model) +
geom_errorbar(aes(ymin = lower95, ymax = upper95, color = dir_eff),
width = 0.25) +
geom_pointrange(aes(ymin = lower95, ymax = upper95, color = dir_eff),
size = 0.5) +
scale_colour_hp_d(option = "Ravenclaw", name = "direction of effect") +
geom_hline(yintercept = 1, linetype = 2) +
labs(title = "Metabolites & asthma status",
x = "Metabolite", y = "Odds Ratio (95% Confidence Interval)") +
theme_minimal() +
theme(title = element_text(size = 16),
strip.text = element_text(size = 12),
axis.title = element_text(size = 12),
axis.text = element_text(size = 12),
legend.position = "bottom",
legend.title = element_text(size = 12),
legend.text = element_text(size = 12)) +
scale_y_continuous(trans = log_trans(),
breaks = axisTicks(log(range(res_long[metabolite %in% sm_list, or])),
log = TRUE)) +
coord_flip()
ggsave(here(fig_dir, "sm_or95ci.jpg"), width = 14, height = 7)
ggplot(res_long[metabolite %in% cer_list, ], aes(x = rev_metabolite, y = or)) +
facet_grid(. ~ model) +
geom_errorbar(aes(ymin = lower95, ymax = upper95, color = dir_eff),
width = 0.25) +
geom_pointrange(aes(ymin = lower95, ymax = upper95, color = dir_eff),
size = 0.5) +
scale_colour_hp_d(option = "Ravenclaw", name = "direction of effect") +
geom_hline(yintercept = 1, linetype = 2) +
labs(title = "Metabolites & asthma status",
x = "Metabolite", y = "Odds Ratio (95% Confidence Interval)") +
theme_minimal() +
theme(title = element_text(size = 16),
strip.text = element_text(size = 12),
axis.title = element_text(size = 12),
axis.text = element_text(size = 12),
legend.position = "bottom",
legend.title = element_text(size = 12),
legend.text = element_text(size = 12)) +
scale_y_continuous(trans = log_trans(),
breaks = axisTicks(log(range(res_long[metabolite %in% cer_list, or])),
log = TRUE)) +
coord_flip()
ggsave(here(fig_dir, "cer_or95ci.jpg"), width = 14, height = 7)
ggplot(res_long[metabolite %in% ceroth_list, ], aes(x = rev_metabolite, y = or)) +
facet_grid(. ~ model) +
geom_errorbar(aes(ymin = lower95, ymax = upper95, color = dir_eff),
width = 0.25) +
geom_pointrange(aes(ymin = lower95, ymax = upper95, color = dir_eff),
size = 0.5) +
scale_colour_hp_d(option = "Ravenclaw", name = "direction of effect") +
geom_hline(yintercept = 1, linetype = 2) +
labs(title = "Metabolites & asthma status",
x = "Metabolite", y = "Odds Ratio (95% Confidence Interval)") +
theme_minimal() +
theme(title = element_text(size = 16),
strip.text = element_text(size = 12),
axis.title = element_text(size = 12),
axis.text = element_text(size = 12),
legend.position = "bottom",
legend.title = element_text(size = 12),
legend.text = element_text(size = 12)) +
scale_y_continuous(trans = log_trans(),
breaks = axisTicks(log(range(res_long[metabolite %in% ceroth_list, or])),
log = TRUE)) +
coord_flip()
ggsave(here(fig_dir, "ceroth_or95ci.jpg"), width = 14, height = 7)
ggplot(res_unused[metabolite %in% sphoth_list, ], aes(x = rev_metabolite, y = or)) +
facet_grid(. ~ model) +
geom_errorbar(aes(ymin = lower95, ymax = upper95, color = dir_eff),
width = 0.25) +
geom_pointrange(aes(ymin = lower95, ymax = upper95, color = dir_eff),
size = 0.5) +
scale_colour_hp_d(option = "Ravenclaw", name = "direction of effect") +
geom_hline(yintercept = 1, linetype = 2) +
labs(title = "Metabolites & asthma status",
x = "Metabolite", y = "Odds Ratio (95% Confidence Interval)") +
theme_minimal() +
theme(title = element_text(size = 16),
strip.text = element_text(size = 12),
axis.title = element_text(size = 12),
axis.text = element_text(size = 12),
legend.position = "bottom",
legend.title = element_text(size = 12),
legend.text = element_text(size = 12)) +
scale_y_continuous(trans = log_trans(),
breaks = axisTicks(log(range(res_unused[metabolite %in% sphoth_list, or])),
log = TRUE)) +
coord_flip()
ggsave(here(fig_dir, "sphoth_or95ci_min_change_crd.jpg"), width = 14, height = 7)
ggplot(res_unused[metabolite %in% sm_list, ], aes(x = rev_metabolite, y = or)) +
facet_grid(. ~ model) +
geom_errorbar(aes(ymin = lower95, ymax = upper95, color = dir_eff),
width = 0.25) +
geom_pointrange(aes(ymin = lower95, ymax = upper95, color = dir_eff),
size = 0.5) +
scale_colour_hp_d(option = "Ravenclaw", name = "direction of effect") +
geom_hline(yintercept = 1, linetype = 2) +
labs(title = "Metabolites & asthma status",
x = "Metabolite", y = "Odds Ratio (95% Confidence Interval)") +
theme_minimal() +
theme(title = element_text(size = 16),
strip.text = element_text(size = 12),
axis.title = element_text(size = 12),
axis.text = element_text(size = 12),
legend.position = "bottom",
legend.title = element_text(size = 12),
legend.text = element_text(size = 12)) +
scale_y_continuous(trans = log_trans(),
breaks = axisTicks(log(range(res_unused[metabolite %in% sm_list, or])),
log = TRUE)) +
coord_flip()
ggsave(here(fig_dir, "sm_or95ci_min_change_crd.jpg"), width = 14, height = 7)
ggplot(res_unused[metabolite %in% cer_list, ], aes(x = rev_metabolite, y = or)) +
facet_grid(. ~ model) +
geom_errorbar(aes(ymin = lower95, ymax = upper95, color = dir_eff),
width = 0.25) +
geom_pointrange(aes(ymin = lower95, ymax = upper95, color = dir_eff),
size = 0.5) +
scale_colour_hp_d(option = "Ravenclaw", name = "direction of effect") +
geom_hline(yintercept = 1, linetype = 2) +
labs(title = "Metabolites & asthma status",
x = "Metabolite", y = "Odds Ratio (95% Confidence Interval)") +
theme_minimal() +
theme(title = element_text(size = 16),
strip.text = element_text(size = 12),
axis.title = element_text(size = 12),
axis.text = element_text(size = 12),
legend.position = "bottom",
legend.title = element_text(size = 12),
legend.text = element_text(size = 12)) +
scale_y_continuous(trans = log_trans(),
breaks = axisTicks(log(range(res_unused[metabolite %in% cer_list, or])),
log = TRUE)) +
coord_flip()
ggsave(here(fig_dir, "cer_or95ci_min_change_crd.jpg"), width = 14, height = 7)
ggplot(res_unused[metabolite %in% ceroth_list, ], aes(x = rev_metabolite, y = or)) +
facet_grid(. ~ model) +
geom_errorbar(aes(ymin = lower95, ymax = upper95, color = dir_eff),
width = 0.25) +
geom_pointrange(aes(ymin = lower95, ymax = upper95, color = dir_eff),
size = 0.5) +
scale_colour_hp_d(option = "Ravenclaw", name = "direction of effect") +
geom_hline(yintercept = 1, linetype = 2) +
labs(title = "Metabolites & asthma status",
x = "Metabolite", y = "Odds Ratio (95% Confidence Interval)") +
theme_minimal() +
theme(title = element_text(size = 16),
strip.text = element_text(size = 12),
axis.title = element_text(size = 12),
axis.text = element_text(size = 12),
legend.position = "bottom",
legend.title = element_text(size = 12),
legend.text = element_text(size = 12)) +
scale_y_continuous(trans = log_trans(),
breaks = axisTicks(log(range(res_unused[metabolite %in% ceroth_list, or])),
log = TRUE)) +
coord_flip()
ggsave(here(fig_dir, "ceroth_or95ci_min_change_crd.jpg"), width = 14, height = 7)
sessionInfo()
## R version 3.6.0 (2019-04-26)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: CentOS release 6.10 (Final)
##
## Matrix products: default
## BLAS: /app/R-3.6.0@i86-rhel6.0/lib64/R/lib/libRblas.so
## LAPACK: /app/R-3.6.0@i86-rhel6.0/lib64/R/lib/libRlapack.so
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] scales_1.1.0 harrypotter_2.1.0 ggrepel_0.8.1 here_0.1
## [5] data.table_1.12.8 forcats_0.4.0 stringr_1.4.0 dplyr_0.8.4
## [9] purrr_0.3.3 readr_1.3.1 tidyr_1.0.2 tibble_2.1.3
## [13] ggplot2_3.2.1 tidyverse_1.3.0
##
## loaded via a namespace (and not attached):
## [1] tidyselect_1.0.0 xfun_0.12 reshape2_1.4.3 haven_2.2.0
## [5] lattice_0.20-38 colorspace_1.4-1 vctrs_0.2.2 generics_0.0.2
## [9] htmltools_0.4.0 yaml_2.2.1 rlang_0.4.4 pillar_1.4.3
## [13] withr_2.1.2 glue_1.3.1 DBI_1.1.0 dbplyr_1.4.2
## [17] modelr_0.1.5 readxl_1.3.1 plyr_1.8.5 lifecycle_0.1.0
## [21] munsell_0.5.0 gtable_0.3.0 cellranger_1.1.0 rvest_0.3.5
## [25] evaluate_0.14 labeling_0.3 knitr_1.28 fansi_0.4.1
## [29] broom_0.5.4 Rcpp_1.0.3 backports_1.1.5 jsonlite_1.6.1
## [33] farver_2.0.3 fs_1.3.0 gridExtra_2.3 hms_0.5.3
## [37] digest_0.6.23 stringi_1.4.5 rprojroot_1.3-2 grid_3.6.0
## [41] cli_2.0.1 tools_3.6.0 magrittr_1.5 lazyeval_0.2.2
## [45] crayon_1.3.4 pkgconfig_2.0.3 xml2_1.2.2 reprex_0.3.0
## [49] lubridate_1.7.4 assertthat_0.2.1 rmarkdown_2.1 httr_1.4.1
## [53] rstudioapi_0.11 R6_2.4.1 nlme_3.1-144 compiler_3.6.0